### Description

`dCommSignif`

is supposed to test the significance of communities
within a graph. For a community of the graph, it first calculates two
types of degrees for each node: degrees based on parters only within
the community itself, and the degrees based on its parters NOT in the
community but in the graph. Then, it performs two-sample Wilcoxon tests
on these two types of degrees to produce the signficance level
(p-value)

### Usage

dCommSignif(g, comm)

### Arguments

- g
- an object of class "igraph" or "graphNEL"
- comm
- an object of class "communities". Details on this class can
be found at http://igraph.org/r/doc/communities.html

### Value

`significance`

: a vector of p-values (significance)

### Note

none

### Examples

# 1) generate an vector consisting of random values from beta distribution
x <- rbeta(1000, shape1=0.5, shape2=1)
# 2) fit a p-value distribution under beta-uniform mixture model
fit <- dBUMfit(x, ntry=1, hist.bum=FALSE, contour.bum=FALSE)

** A total of p-values: 1000**
** Maximum Log-Likelihood: 363.1**
** Mixture parameter (lambda): 0.079**
** Shape parameter (a): 0.454**
# 3) calculate the scores according to the fitted BUM and fdr=0.01
# using "pdf" method
scores <- dBUMscore(fit, method="pdf", fdr=0.05, scatter.bum=FALSE)
names(scores) <- as.character(1:length(scores))
# 4) generate a random graph according to the ER model
g <- erdos.renyi.game(1000, 1/100)
# 5) produce the induced subgraph only based on the nodes in query
subg <- dNetInduce(g, V(g), knn=0)
# 6) find the module with the maximum score
module <- dNetFind(subg, scores)
# 7) find the module and test its signficance
comm <- walktrap.community(module, modularity=TRUE)
significance <- dCommSignif(module, comm)